Hubungan Kuat Beton Karakteristik Dengan Standar Deviasi “S”

Beton merupakan bahan konstruksi yang terdiri dari semen , pasir, kerikil yang dicampur dan diberi air secukupnya ( water semen rasio) sehingga membentuk suatu adonan (mortal) yang kemudian mengeras menjadi beton yang keras dan mempunyai sifat yang khas. Adukan beton tersebut dimasukkan dalam cetakan yang berbentuk kubus atau silender dengan ukuran tertentu untuk dibuat beberapa benda uji dan setelah berumur dua hari cetakan dibuka dan benda uji direndam dalam air selama 7, 14, 21, dan 28 hari, kemudian diangkat untuk dilakukan pengetesan untuk mendapatkan kekuatan tekan dari masing-masing benda uji tersebut.Jika dari sejumlah besar benda uji tersebut, nilai kuat tekannya menyebar pada nilai rata-rata tertentu, maka secara global telah diakui bahwa penyebaran nilai kuat tekan beton tersebut mengikuti hukum distribusi statistik normal atau disebut juga dengan distribusi gauss, dimana kurva distribusi normal ini menyebar secara symetris terhadap nilai rata-rata dengan current margin sebesar 1,64 kali standar deviasinya (S). Kuat tekan beton dipengaruhi oleh kuat tekan material (pasir , kerikil) , komposisi material, semen dan water semen ratio. Kuat karakteristik dari material adalah suatu nilai kekuatan yang diperoleh secara analisa statistik dari hasil pemeriksaan benda uji., begitu juga dengan kuat tekan beton karakteristik terbatas sampailima5 % saja berada di bawah kuat tekan beton karakteristik. Hal ini menunjukkan bahwa kuat tekan beton karakteristik berhubungan langsung dengan standar deviasi (S), yaitu pada suatu tingkat tertentu ,standar deviasi (S) akan terus bertambah besar sebanding dengan bertambahnya kuat tekan beton karakteristik hingga mendekati suatu harga tertentu

Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function

Semarang city is one of the busiest city in Indonesia. Doe to its role as the capital city of Central Java, Semarang is known as having a relativity high rate economic activities. The geographic of Semarang city bordered by the Java sea, thus whenever the rainfall is high, there could be flood at certain area. Therefore, prediction of rainfall is very important. Support vector machine (SVM) is one of the most popular methods in nonlinear approach. One of the branches of this method for prediction is support vector regression (SVR). SVR can be approached by quadratic loss function. The study is focus on Semarang rainfall prediction during 2009 to 2013 using several kernel function. Kernel Function can provide optimal weight Some of kernel functions are linear, polynomial, and Radial Basis Function (RBF). Using this method, the study provide 71.61% R-square in the training data, for C parameter 2 with polynomial (p=2), and 71.46% R-square for the testing dat

Analisis Partisipasi Masyarakat dan Pemerintah dalam Pengembangan Sektor Pariwisata Kabupaten Bone, Sulawesi Selatan

The tourism sector is indeed quite promising to help increase foreign exchange reserves and pragmatically also able to increase people's incomes, sustainable tourism development is defined as a tourism development process oriented to the preservation of resources needed for future development where the surrounding community acts as a provider of tourist needs services. Humanitarian factors and local cultural entities should not be ignored, meaning that community life should not be uprooted from its cultural roots only because of the commercial emphasis of tourism, the lifestyle and culture of local communities, so researchers want to see how the role of community and government participation in development tourism as a leading destination and how tourism development is realized as a superior destination

Evaluasi Produksi Overburden pada Front Kerja Excavator Hitachi Shovel

Kegiatan pembongkaran dan pemindahan overburden, dibutuhkan sejumlah alat berat, berupa alat angkut dan alat muat serta alat support. Kegiatan pembongkaran overburden dilakukan pada front kerja excavator hitachi ex.3600 tepatnya pada pada Kabupaten Berau Provinsi Kalimantan Timur. Tujuan yang ingin dicapai dari penelitian ini adalah untuk mengetahui faktor-faktor penyebab tidak tercapainya produksi dan dapat mengetahui solusi atau langkah penyelesaian dari faktor-faktor atau masalah tidak tercapainya produktivitas. Pengambilan data dilakukan dengan cara mengamati lansung area kerja pada excavator hitachi ex3600.02 shovel dengan cara mencatat segala jenis masalah dan waktu terjadinya masalah yang di alami yaitu berupa masalah-masalah yang dapat mempengaruhi target produksi. Kemudian, data yang diperoleh ini, di sesuaikan dengan data poroduksi yang diperoleh pada dispatch. Berdasarkan dari hasil evaluasi yang dilakukan pada bulan Agustus dan September 2016, produksi overburden selalu mengalami Loss yang cukup besar, yaitu pada bulan Agustus pada shift 1 sebesar 14.485,41 ton, pada shift 2 sebesar 20.764,08 ton, dan pada shift 3, Loss sebesar 29.841,7 ton. Pada bulan September shift 1 diperoleh Loss sebesar 28.786,43 ton, shift 2 sebesar 31.082 ton, dan pada shift 3 diperoleh Loss sebesar 37.663,99 ton. Faktor penyebab tidak tercapainya produksi pada bulan Agustus, dapat diketahui 11 masalah yaitu kondisi front, operator makan dan istrahat, pergantian shift, visibility, fatigue, skill operator, fragmentasi, material keras nonblasting, equipment problem, refuel, dan pindah pit dan juga foreman lebih memperhatikan kondisi front yang merupakan area terdepan dalam hal pengangkutan overburden, sehingga alat dapat bekerja dengan maksimal

PENINGKATAN KEMAMPUAN GENERALISASI SISWA MELALUI PENERAPAN MODEL PEMBELAJARAN DISCO NUMBER

PENINGKATAN KEMAMPUAN GENERALISASI SISWA MELALUI PENERAPANMODEL PEMBELAJARAN DISCO NUMBE

Periphyton Community Structure in the Seagrass Ecosystem of the Malang Rapat Village Coast, Bintan Regency, Kepulauan Riau Province

Periphyton is a type of algae that commonly inhabit the surface of immersed objects. A research aims to understand the periphyton community (types and abundance) in the seagrass ecosystem of the Malang Rapat Village has been conducted in May 2015. Samplings were conducted 3 times in 3 stations. In each station there were 3 transeck lines with 5 quadrants (1 m x 1 m) in each line. Periphyton samples were taken by scraping the seagrass surface by tooth brush in the area of 5x2 cm2. Periphyton collected were then preserved in lugol 2-3 drop/40 ml and then was identified.Result shown that there were 33 periphyton species present and they are belonged to 4 classes, namely Bacillariophyceae (28 species), Chlorophyceae (2 species), Cyanophyceae (2 species) and Dinophyceae (1 species). Periphyton abundance was 13,976-29,228 cells/ cm2. In general, the H' was 3.941-4.252; C was 0.063-0.090 and E was 0.815-0.873. The water quality parameters are as follows: temperature was 30.67-31.34 0C; brightness was 0.27-0.35 m; turbidity was 1.39-1.79 NTU; current speed was 0.11-0.12 m/s; depth was 0.27-0.35 m; pH was 8; DO was 7.60-7.96 mg/L; free CO2 was 4.3-4.8 mg/L; salinity was 30.4-30.67 ‰; nitrate was 0.026-0.031 mg/L and phosphate was 0.048-0.123 mg/L. Based on periphyton community, it can be concluded that the aquatic environment in the waters of Trikora Beach, Malang Rapat Village is balance

Analisis Sumber-sumber Pendapatan Daerah Kabupaten Dan Kota Di Jawa Tengah Dengan Metode Geographically Weighted Principal Components Analysis (Gwpca)

The districts/cities sources of revenue in Central Java consists of Natural Revenue District (PAD), the equalization fund (DAPER), and other local income. PAD consists of four variables namely local tax (X1) , retribution (X2) , the results of regional company and wealth management that is separated (X3) , and other legal PAD (X4). DAPER consists of four variables namely sharing of tax revenue (X5) , sharing of non-tax revenue (X6) , the general allocation fund (X7) , and the special allocation fund (X8). Other region revenues (X9) is a source of local income that is not included in the PAD or DAPER. Sources of local revenue variables are mutually correlated multivariate data and have spatial effect. Therefore Geographically Weighted Principal Components Analysis (GWPCA) is suitable for analyzing sources of local revenue variables. GWPCA is a multivariate analysis method that is used to eliminate multicolliniearity in the multivariate data that have spatial effect. The result of this study is that the variables of revenue sources on each location can be replaced by three new variables called PC1, PC2, and PC3 which is independent each other. Variance Cumulative Proportion that can be explained by those new variables is approximately 80%. Based on the first principal component (PC1) that have variance proportion approximately 50%, there are three groups which has different carracteristics. The first group is the region that the revenue have influenced by variables X9 followed by X1. The second group is the region that the revenue have influenced by variables X9 followed by X2. The third group is the region that the revenue have influenced by variables X9 followed by X5. It is also seen that Kudus District has the most distinct characteristics which the revenue are influenced by variables X5 followed by X9